An inability to manage marketing departments and campaign traffic doesn't depend on better AI models or smarter agents; it comes down to clearer coordination.
Picture dozens of aircraft lined up on a runway, engines running, pilots ready for takeoff, but no one in the control tower. Which means there's no coordinated system to guide these waiting pilots safely into the air, no one to manage landings and takeoff patterns, and no one to prevent collisions.
This concerning scenario describes the situation most enterprise marketers find themselves in today—piloting blindly through AI implementation.
As organizations have rushed to embed AI across their departments, many marketers are now regularly using generative AI. Still, most of these initiatives have yet to achieve meaningful scale.
It's still early days of AI adoption, but as we approach the third anniversary of ChatGPT ushering large language models (LLMs) into the mainstream, marketers' efforts remain isolated experiments. Even the most practical and powerful AI marketing use cases feel disconnected, and like those unguided planes, they may never leave the ground.
The problem isn't the technology. We have incredibly sophisticated AI models and capable AI agents. The missing piece is orchestration.
Marketers and agencies, like all businesses trying to prove their AI adaptability, must develop systematic coordination, risk management, and an integration framework that transforms scattered, ad hoc AI pilots into something that can drive enterprise-wide impact.
The Pilot Purgatory Problem
Marketing teams are currently experimenting with a range of AI platforms and tools like ChatGPT and Claude for content creation. IT departments deploy their own AI tools for system optimization. Customer service implements chatbots. Sales teams use AI for lead scoring. Each initiative shows promise in isolation, but together they create a fragmented mess.
That's not all. Consider the typical enterprise martech stack—companies license 80 to 120 different platforms to run their marketing and ad operations. Each platform requires user accounts, data loading, configuration, and integration work. Employees routinely log into multiple systems to complete basic tasks, manually moving data and insights between disconnected tools.
Now imagine trying to layer AI on top of this already complex ecosystem, one platform at a time. The result is predictable: AI agents that can't communicate with each other, duplicate efforts across departments, compliance nightmares, and ROI that remains frustratingly unclear because there's no unified measurement framework.
I return to the runway nightmare analogy to get a handle on the situation clients and partners find themselves in when dealing with agencies and marketers. I recently tested travel booking engine Kayak's new AI booking assistant. I asked it to recommend flights from Chicago to New York designed so I could easily make a morning meeting.
Despite providing a clear prompt with context, the system had no idea what to do with my request. Why? Because it exists in isolation. Kayak's AI assistant was not connected to my calendar. It was unaware of my airline preferences. And it had no understanding of ground transportation or meeting logistics. Making this request was like asking a pilot to navigate without instruments, communication, or a flight plan.
This exercise in frustration parallels the experience enterprise clients feel when trying to work with the AI tools their agencies and partners provide.
Building Your AI Control Tower
True AI orchestration requires what I call the "control tower" approach. It starts with a systematic framework that operates one layer above individual AI tools and then coordinates all the activities under it just as air traffic control manages aircraft.
The foundation starts with what we might call the "switchboard," which includes APIs and integration architecture that connect all your systems and enable seamless data translation between platforms.
Say I want to target men aged 50 in the northeast. If my activation platform reads gender as "M" for males, there needs to be automatic translation so all formats are on the same page.
This is where the AI control tower concept comes in. You need:
- Unified governance: Create a single framework for AI ethics, compliance, and risk management across the enterprise
- Task coordination: Combine the right AI tools for specific tasks with clear human oversight
- Real-time monitoring: Define performance tracking, bias detection, and quality control
- Change management: Get training, adoption support, and feedback loops for continuous improvement
Take audience activation as an example. An orchestrated approach would use AI agents as connective tissue between systems by automating handoffs between your CDP, activation platforms, and reporting tools while maintaining strategic oversight.
The payoff for getting orchestration right means faster time-to-value for AI investments and higher adoption rates across the organization. Altogether, that's a real competitive advantage.
Start With Your Foundation
Too many organizations are skipping their foundation and jumping straight to a solution. They want a Ferrari, but end up with a bicycle. While both have wheels, the performance gap is enormous.
It's often hard to appreciate the complexities within an enterprise—each is different in very important ways. But there are commonalities. Interdependencies within organizations aren't always visible, for example. Successfully implementing AI at enterprise scale requires understanding these connections and building the infrastructure to support coordinated action.
Above all, you have to be in control of your control tower. This isn't about waiting for each of your 80 to 120 platforms to update their AI capabilities. That approach will take years and result in a patchwork of disconnected solutions.
Instead, you must seize your own AI destiny by building the orchestration layer that connects, coordinates, and optimizes AI deployment across your entire organization.
Keep in mind that technology isn't the bottleneck; the organization itself often is. The companies that deal with organizational bottlenecks and institute stable AI orchestration will be the ones that truly transform their operations and leave their competitors grounded on the runway.
More Resources on AI Implementation Strategies
How CMOs Can Use AI to Make Career-Changing, Strategic Business Impact
AI Growth Statistics: The Transformative Impact of Artificial Intelligence
From AI Hype to Meaningful Marketing Results: Start With These Three Key Changes
